{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8d97365b-fe57-446b-8316-b42bec6828f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os.path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.special import erf\n",
    "\n",
    "import refnx\n",
    "from refnx.dataset import ReflectDataset, Data1D\n",
    "from refnx.reflect import SLD, MaterialSLD, ReflectModel, Component\n",
    "from refnx.analysis import Objective, CurveFitter, Transform, possibly_create_parameter\n",
    "from refnx.analysis import Parameter, Parameters\n",
    "from refnx.reflect import SLD, Slab, Structure, ReflectModel\n",
    "\n",
    "from random import random\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "11c267a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#correct implementation\n",
    "class PTCDI(Component):\n",
    "    def __init__(\n",
    "        self,\n",
    "        nlayers,\n",
    "        monolayer_thickness,\n",
    "        rel_roughness,\n",
    "        sld_box1,   # <----- this is closest to the phase layer\n",
    "        sld_box2,\n",
    "        thick_frac,\n",
    "        sig1_rel_pos,  # <------ counting from backing to fronting direction\n",
    "        sig1_width,\n",
    "        sig2_rel_pos,  # <------ counting from backing to fronting direction\n",
    "        sig2_width,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.nlayers = possibly_create_parameter(nlayers)\n",
    "        self.monolayer_thickness = possibly_create_parameter(monolayer_thickness)\n",
    "        self.rel_roughness = possibly_create_parameter(rel_roughness)\n",
    "        self.sld_box1 = possibly_create_parameter(sld_box1)\n",
    "        self.sld_box2 = possibly_create_parameter(sld_box2)\n",
    "        self.thick_frac = possibly_create_parameter(thick_frac)\n",
    "        self.sig1_rel_pos = possibly_create_parameter(sig1_rel_pos)\n",
    "        self.sig1_width = possibly_create_parameter(sig1_width)\n",
    "        self.sig2_rel_pos = possibly_create_parameter(sig2_rel_pos)\n",
    "        self.sig2_width = possibly_create_parameter(sig2_width)\n",
    "\n",
    "    @property\n",
    "    def parameters(self):\n",
    "        return Parameters([\n",
    "            self.nlayers,\n",
    "            self.monolayer_thickness,\n",
    "            self.rel_roughness,\n",
    "            self.sld_box1,\n",
    "            self.sld_box2,\n",
    "            self.thick_frac,\n",
    "            self.sig1_rel_pos,\n",
    "            self.sig1_width,\n",
    "            self.sig2_rel_pos,\n",
    "            self.sig2_width,\n",
    "        ])\n",
    "            \n",
    "    def slabs(self, structure=None):\n",
    "        nlayers = int(np.round(self.nlayers.value))\n",
    "        monolayer_thickness = self.monolayer_thickness.value\n",
    "        rel_roughness = self.rel_roughness.value        \n",
    "        s_width1 = self.sig1_width.value\n",
    "        s_width2 = self.sig2_width.value\n",
    "        s_start1 = self.sig1_rel_pos.value\n",
    "        s_start2 = self.sig2_rel_pos.value\n",
    "        sld1 = self.sld_box1.value\n",
    "        sld2 = self.sld_box2.value\n",
    "        diff = sld2 - sld1\n",
    "        mini, maxi = min(sld1, sld2), max(sld1, sld2)\n",
    "\n",
    "        slabs = np.zeros((nlayers * 2, 5))\n",
    "        t1 = monolayer_thickness * self.thick_frac.value\n",
    "        t2 = monolayer_thickness * (1 - self.thick_frac.value)\n",
    "        slabs[:, 0] = np.array([t1, t2] * nlayers)\n",
    "\n",
    "        # why logistic, why not erf?\n",
    "        def logistic(x, mini, maxi, k, x0):\n",
    "            #return mini + (maxi - mini) / (1 + np.exp(-k * (x - x0)))\n",
    "            return 0.5 * (erf(-k * (x - x0))) + 0.5\n",
    "\n",
    "        idx = np.arange(nlayers * 2) / 2\n",
    "        \n",
    "        # note that the first monolayer has idx=0, and the last monolayer has idx=29.\n",
    "        # if your indexing starts from 1, then sig1_rel_pos will be different by 1.\n",
    "        sigmoid1 = logistic(idx, 1, 0, s_width1, s_start1)\n",
    "        sigmoid2 = logistic(idx, 1, 0, s_width2, s_start2)\n",
    "        #for i in range(45):\n",
    "        #    if idx[i] > s_start2+2:\n",
    "        #        sigmoid2[i] = 0      \n",
    "\n",
    "        avg_sld = (sld1 + sld2) / 2\n",
    "        diff = maxi - mini\n",
    "\n",
    "        idx = np.arange(nlayers)\n",
    "\n",
    "        if sld1 > sld2:\n",
    "            slabs[2 * idx, 1] = sigmoid1 * ((maxi - avg_sld)*sigmoid2 + avg_sld)\n",
    "            slabs[2*idx + 1, 1] = sigmoid1 * ((avg_sld - mini)*(1 - sigmoid2) + mini)\n",
    "        else:\n",
    "            slabs[2*idx + 1, 1] = sigmoid1[2*idx + 1] * ((maxi - avg_sld)*sigmoid2[2*idx + 1] + avg_sld)\n",
    "            slabs[2 * idx, 1] = sigmoid1[2*idx] * ((avg_sld - mini)*(1 - sigmoid2[2*idx]) + mini)\n",
    "\n",
    "        slabs[:, 3] = rel_roughness * monolayer_thickness\n",
    "        # fronting medium comes first\n",
    "        return slabs[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a532eb12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a demonstration cell.\n",
    "#         nlayers,\n",
    "#         monolayer_thickness,\n",
    "#         rel_roughness\n",
    "#         sld_box1,\n",
    "#         sld_diff,\n",
    "#         thick_frac,\n",
    "#         sig1_rel_pos,\n",
    "#         sig1_width,\n",
    "#         sig2_rel_pos,\n",
    "#         sig2_width,\n",
    "#         sig22_width,\n",
    "\n",
    "c = PTCDI(50, 10, 0.1, 5, 15, 0.5, 30, 0.16, 20, 0.2)\n",
    "s = SLD(0) | c | SLD(8)\n",
    "# print(c.slabs())\n",
    "s.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c20ccd24-62db-426f-8bbb-730496b0840d",
   "metadata": {},
   "outputs": [],
   "source": [
    "air = SLD(0)\n",
    "\n",
    "si = SLD(21.861 + 0.103j, \"Si\")\n",
    "sio2 = SLD(21.2632+0.058j, \"SiO2\")\n",
    "\n",
    "sio2_l = sio2(47, 1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8339343f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:01<00:00, 335.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5.2765 13.5512 22.7838  3.7299 13.6411  3.1639  8.3575]\n",
      "(500, 7)\n",
      "(500, 256)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from multiprocessing import  Process, Queue, Lock\n",
    "\n",
    "batch_size = 500\n",
    "\n",
    "curves = []\n",
    "q2 = np.linspace(0.015, 0.35, 256)\n",
    "#Parameters\n",
    "\n",
    "sld_box1 = np.random.uniform(low=4, high=8.5, size=batch_size)\n",
    "sld_box2 = np.random.uniform(low=1, high=2, size=batch_size)\n",
    "\n",
    "\n",
    "mono_thick = np.random.uniform(low=19.5, high=23.5, size=batch_size)\n",
    "\n",
    "sig1_width = np.random.uniform(low=0.1, high=7, size=batch_size)\n",
    "sig1_rel_pos = np.random.uniform(low=-0.2, high=30, size=batch_size)\n",
    "\n",
    "sig2_width = np.random.uniform(low=0.5, high=3, size=batch_size)\n",
    "sig2_rel_pos = np.random.uniform(low=1.5, high=29, size=batch_size)\n",
    "\n",
    "\n",
    "for i in tqdm(range(batch_size)):\n",
    "    \n",
    "    sld_box2[i] = np.random.uniform(low=17-sld_box1[i]-3, high=17-sld_box1[i]+2, size=None)\n",
    "    if sig1_rel_pos[i] <= 4:  \n",
    "        sig2_rel_pos[i] = np.random.uniform(low=-0.15, high=sig1_rel_pos[i], size=None)\n",
    "    if sig1_rel_pos[i] > 4 and sig1_rel_pos[i] <= 7:\n",
    "        sig2_rel_pos[i] = np.random.uniform(3, sig1_rel_pos[i]+0.5, size=None)\n",
    "    if sig1_rel_pos[i] > 7 and sig1_rel_pos[i] <= 12:\n",
    "        sig2_rel_pos[i] = np.random.uniform(6, sig1_rel_pos[i]+0.5, size=None)\n",
    "    if sig1_rel_pos[i] > 12 and sig1_rel_pos[i] <= 15:\n",
    "        sig2_rel_pos[i] = np.random.uniform(7, sig1_rel_pos[i]+0.5, size=None)\n",
    "    if sig1_rel_pos[i] > 15:\n",
    "        sig2_rel_pos[i] = np.random.uniform(9, sig1_rel_pos[i]+2, size=None)\n",
    "    \n",
    "    if sig2_rel_pos[i] <= 0:\n",
    "        sig2_width[i] = 1/5\n",
    "    if sig2_rel_pos[i] > 0 and sig2_rel_pos[i] <= 4:\n",
    "        sig2_width[i] = np.random.uniform(1/5, 1/0.5, size=None)\n",
    "    if sig2_rel_pos[i] > 4 and sig2_rel_pos[i] <= 7:\n",
    "        sig2_width[i] = np.random.uniform(1/4, 1/0.2, size=None)\n",
    "    if sig2_rel_pos[i] > 7:\n",
    "        sig2_width[i] = np.random.uniform(1/4, 1/0.1, size=None)\n",
    "    \n",
    "    # sig1 width\n",
    "    if sig1_rel_pos[i] <= 0:\n",
    "        sig1_width[i] = 1/5\n",
    "    if sig1_rel_pos[i] > 0 and sig1_rel_pos[i] <= 3:\n",
    "        sig1_width[i] = np.random.uniform(1/5, high=1/0.8, size=None)\n",
    "    if sig1_rel_pos[i] > 3 and sig1_rel_pos[i] <= 6:\n",
    "        sig1_width[i] = np.random.uniform(1/5, high=1/0.5, size=None)\n",
    "    if sig1_rel_pos[i] > 6 and sig1_rel_pos[i] <= 14:\n",
    "        sig1_width[i] = np.random.uniform(low=1/4, high=1/0.22, size=None)\n",
    "    if sig1_rel_pos[i] > 14:\n",
    "        sig1_width[i] = np.random.uniform(low=1/2.5, high=1/0.22, size=None)\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "    pol_slab = PTCDI(50, #nlayers,\n",
    "                    mono_thick[i].round(4), #monolayer_thickness,\n",
    "                    0.235, #rel_roughness\n",
    "                    sld_box1[i].round(4), #sld_box1\n",
    "                    sld_box1[i].round(4)+sld_box2[i].round(4), #sld_box2\n",
    "                    0.5, #thick_frac,\n",
    "                    sig1_rel_pos[i].round(4), #sig1_rel_pos,\n",
    "                    1/sig1_width[i].round(4), #sig1_width,\n",
    "                    sig2_rel_pos[i].round(4), #sig1_rel_pos,\n",
    "                    1/sig2_width[i].round(4) #sig1_width,\n",
    "                    )\n",
    "    poly = SLD(17.0, \"pol_inter\")\n",
    "    inter_l = poly(6.3, 5.59)\n",
    "    s = air | pol_slab | inter_l | sio2_l | si(0, 1)\n",
    "    model = ReflectModel(s, bkg=1e-9, dq=0.01, dq_type='pointwise')\n",
    "    reflectivity = model(q2)\n",
    "    curves.append(reflectivity)\n",
    "\n",
    "labels = np.array([sld_box1.round(4),\n",
    "                sld_box2.round(4),\n",
    "                mono_thick.round(4),\n",
    "                sig1_width.round(4),\n",
    "                sig1_rel_pos.round(4),\n",
    "                sig2_width.round(4),\n",
    "                sig2_rel_pos.round(4)])\n",
    "\n",
    "labels = labels.transpose()\n",
    "\n",
    "labels_all = np.array(labels)\n",
    "curves_all = np.array(curves)\n",
    "\n",
    "print(labels_all[0,:])\n",
    "\n",
    "print(labels_all.shape)\n",
    "print(curves_all.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1a4166cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 7)\n",
      "(500, 256)\n"
     ]
    }
   ],
   "source": [
    "labels_all = np.reshape(labels_all, [500, 7])\n",
    "curves_all = np.reshape(curves_all, [500, 256])\n",
    "curves_all = np.array(curves_all, dtype='float32')\n",
    "\n",
    "print(labels_all.shape)\n",
    "\n",
    "print(curves_all.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cfe9017d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 7)\n",
      "(500, 256)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(labels_all.shape)\n",
    "print(curves_all.shape)\n",
    "\n",
    "for i in range(3): \n",
    "    plt.plot(curves_all[i,:])\n",
    "    plt.yscale('log')\n",
    "    plt.show()\n",
    "\n",
    "np.save('labels', labels_all)\n",
    "np.save('curves', curves_all)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TensorFlow2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
